301 lines
12 KiB
Python
301 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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每日聊天汇总定时任务
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- 每天 23:50 自动统计当日各店铺数据
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- 用 AI 生成自然语言摘要
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- 发送到企业微信 Webhook + QQ 邮件
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"""
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import asyncio
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import os
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import json
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import logging
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from datetime import datetime, date, timedelta
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from typing import Optional
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import httpx
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from dotenv import load_dotenv
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load_dotenv()
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logger = logging.getLogger("cs_agent")
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WECHAT_WEBHOOK = os.getenv("WECHAT_WEBHOOK", "")
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SUMMARY_EMAIL = os.getenv("SUMMARY_EMAIL", "") # 收摘要的邮箱
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SEND_HOUR = int(os.getenv("SUMMARY_HOUR", "23"))
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SEND_MINUTE = int(os.getenv("SUMMARY_MINUTE", "50"))
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# ──────────────────────────────────────────
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# 统计数据整理
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# ──────────────────────────────────────────
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def _build_stats_text(target_date: str = "") -> str:
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"""整理今日数据，返回给 AI 的原始统计文本"""
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from db import chat_log_db as db
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from db.deal_outcome_db import get_daily_summary
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if not target_date:
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target_date = datetime.now().strftime("%Y-%m-%d")
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stats = db.get_daily_stats(target_date)
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convs = db.get_daily_conversations(target_date)
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deal_sum = get_daily_summary(target_date)
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if not stats:
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return f"{target_date} 当日无任何聊天记录。"
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# 按 acc_id 分组对话片段
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conv_map: dict[str, list] = {}
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for c in convs:
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aid = c.get("acc_id") or "未知店铺"
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conv_map.setdefault(aid, []).append(c)
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lines = [f"【{target_date} 各店铺数据】\n"]
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# 成交/未成交汇总（供 AI 摘要与数据分析）
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lines.append("【成交与未成交】")
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lines.append(f" 成交：{deal_sum['成交数']} 笔，金额 {deal_sum['成交金额']:.0f} 元")
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lines.append(f" 未成交：{deal_sum['未成交数']} 笔")
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if deal_sum["未成交原因分布"]:
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for reason, cnt in deal_sum["未成交原因分布"].items():
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lines.append(f" - {reason}：{cnt} 笔")
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if deal_sum["成交明细"]:
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for o in deal_sum["成交明细"][:5]:
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r = "让价后" if o.get("discount_given") else "直接"
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lines.append(f" ✓ {o.get('customer_name', '')[:6]} {r}成交 {o.get('amount', 0):.0f}元")
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if deal_sum["未成交明细"]:
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for o in deal_sum["未成交明细"][:5]:
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lines.append(f" ✗ {o.get('customer_name', '')[:6]} {o.get('reason', '')}")
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lines.append("")
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for s in stats:
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acc = s.get("acc_id") or "未知店铺"
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plat = s.get("platform") or ""
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label = f"{acc}（{plat}）" if plat else acc
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lines.append(f"▶ 店铺：{label}")
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lines.append(f" 接待客户：{s['unique_customers']} 人，共 {s['total_msgs']} 条消息（收 {s['recv']} 发 {s['sent']}）")
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lines.append(f" 首条：{(s.get('first_msg') or '')[-8:-3]} 末条：{(s.get('last_msg') or '')[-8:-3]}")
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shop_convs = conv_map.get(acc, [])
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for c in shop_convs[:6]: # 最多展示6个客户片段
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name = c.get("customer_name") or c.get("customer_id", "")[:8]
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snippet = (c.get("snippet") or "")[:120]
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lines.append(f" · {name}（{c['msg_count']}条）{snippet}")
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if len(shop_convs) > 6:
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lines.append(f" ... 还有 {len(shop_convs)-6} 位客户")
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lines.append("")
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return "\n".join(lines)
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# ──────────────────────────────────────────
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# AI 生成摘要
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# ──────────────────────────────────────────
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async def _ai_summary(raw_text: str) -> str:
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"""调用 AI 把统计文本转成自然语言日报"""
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try:
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from openai import AsyncOpenAI
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client = AsyncOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL"),
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)
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model = os.getenv("OPENAI_MODEL", "doubao-seed-2-0-lite-260215")
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resp = await client.chat.completions.create(
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model=model,
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messages=[
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{
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"role": "system",
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"content": (
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"你是一名电商运营助理。根据下面的客服聊天数据，"
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"为老板写一份简洁的当日运营日报（200字以内）。"
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"要包含：接待总人数、各店铺情况、有无成交或异常情况。"
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"语气轻松，像发给老板的微信消息，不需要标题。"
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),
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},
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{"role": "user", "content": raw_text},
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],
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max_tokens=300,
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temperature=0.5,
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)
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return resp.choices[0].message.content.strip()
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except Exception as e:
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# AI 失败就直接返回原始统计
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return raw_text
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# ──────────────────────────────────────────
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# 推送：企业微信
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# ──────────────────────────────────────────
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async def _send_wechat(content: str):
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"""推送到企业微信群机器人（markdown 格式，单条 ≤4096 字节自动分段）"""
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if not WECHAT_WEBHOOK:
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logger.info("[DailySummary] 未配置 WECHAT_WEBHOOK，跳过推送")
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return
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# 企业微信单条 markdown 限 4096 字节，超长自动分段
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encoded = content.encode("utf-8")
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chunks = []
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while encoded:
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chunk = encoded[:3800].decode("utf-8", errors="ignore")
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chunks.append(chunk)
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encoded = encoded[len(chunk.encode("utf-8")):]
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async with httpx.AsyncClient(timeout=10) as client:
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for i, chunk in enumerate(chunks):
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payload = {"msgtype": "markdown", "markdown": {"content": chunk}}
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try:
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resp = await client.post(WECHAT_WEBHOOK, json=payload)
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data = resp.json()
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if data.get("errcode") == 0:
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logger.info("[DailySummary] 企业微信推送成功（第%s段）", i + 1)
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else:
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logger.warning("[DailySummary] 企业微信推送失败: %s", data)
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except Exception as e:
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logger.exception("[DailySummary] 企业微信推送异常: %s", e)
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# ──────────────────────────────────────────
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# 推送：邮件
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# ──────────────────────────────────────────
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def _send_email(subject: str, body: str):
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"""发送日报邮件"""
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if not SUMMARY_EMAIL:
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return
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try:
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from mail.email_sender import email_sender
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import smtplib
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from email.mime.text import MIMEText
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from email.header import Header
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msg = MIMEText(body, "plain", "utf-8")
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msg["Subject"] = Header(subject, "utf-8").encode()
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msg["From"] = f"{Header(email_sender.sender_name, 'utf-8').encode()} <{email_sender.smtp_user}>"
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msg["To"] = SUMMARY_EMAIL
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with smtplib.SMTP(email_sender.smtp_host, email_sender.smtp_port) as s:
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s.starttls()
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s.login(email_sender.smtp_user, email_sender.smtp_password)
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s.sendmail(email_sender.smtp_user, [SUMMARY_EMAIL], msg.as_string())
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logger.info("[DailySummary] 日报邮件已发送至 %s", SUMMARY_EMAIL)
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except Exception as e:
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logger.exception("[DailySummary] 日报邮件发送失败: %s", e)
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# ──────────────────────────────────────────
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# 企业微信 Markdown 排版
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# ──────────────────────────────────────────
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def _build_wechat_markdown(title: str, ai_text: str, raw_text: str, target_date: str = "") -> str:
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"""
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构建符合企业微信规范的 markdown 内容。
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支持：**bold**、<font color="...">、> 引用、``` 代码块、- 列表
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不支持：<details>、<summary>、HTML 标签（除 font/br）
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"""
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from db import chat_log_db as db
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from db.deal_outcome_db import get_daily_summary
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date = target_date or datetime.now().strftime("%Y-%m-%d")
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stats = db.get_daily_stats(date)
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deal_sum = get_daily_summary(date)
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lines = [f"## {title}\n"]
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# AI 摘要部分
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lines.append("> " + ai_text.replace("\n", "\n> "))
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lines.append("")
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# 成交/未成交
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lines.append("**📈 成交与未成交**")
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lines.append(f"- 成交 **{deal_sum['成交数']}** 笔 · 金额 **{deal_sum['成交金额']:.0f}** 元")
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lines.append(f"- 未成交 **{deal_sum['未成交数']}** 笔")
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if deal_sum["未成交原因分布"]:
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for reason, cnt in deal_sum["未成交原因分布"].items():
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lines.append(f" - {reason}：{cnt} 笔")
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lines.append("")
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# 各店铺数据表格（企业微信不支持 | 表格，用列表代替）
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if stats:
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lines.append("**📋 各店铺明细**")
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for s in stats:
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acc = s.get("acc_id") or "未知店铺"
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plat = s.get("platform") or ""
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label = f"{acc}（{plat}）" if plat else acc
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first = (s.get("first_msg") or "")[-8:-3]
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last = (s.get("last_msg") or "")[-8:-3]
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lines.append(
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f"- <font color=\"info\">{label}</font> "
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f"接待 **{s['unique_customers']}** 人 · "
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f"消息 {s['total_msgs']} 条（收{s['recv']}/发{s['sent']}）"
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f" {first}~{last}"
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)
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lines.append("")
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lines.append(f"<font color=\"comment\">发送时间：{datetime.now().strftime('%H:%M:%S')}</font>")
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return "\n".join(lines)
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# ──────────────────────────────────────────
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# 主入口：生成并推送日报
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# ──────────────────────────────────────────
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async def send_daily_summary(target_date: str = ""):
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"""生成并推送当日汇总"""
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if not target_date:
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target_date = datetime.now().strftime("%Y-%m-%d")
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logger.info("[DailySummary] 开始生成 %s 日报...", target_date)
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raw_text = _build_stats_text(target_date)
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ai_text = await _ai_summary(raw_text)
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title = f"📊 {target_date} 客服日报"
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# ── 企业微信 markdown（不支持 <details>，用标准语法）──
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wechat_md = _build_wechat_markdown(title, ai_text, raw_text, target_date)
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await _send_wechat(wechat_md)
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# ── 邮件：纯文本 ──
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email_body = f"{ai_text}\n\n{'='*40}\n\n{raw_text}"
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_send_email(title, email_body)
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logger.info("[DailySummary] 日报推送完成")
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return ai_text
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# ──────────────────────────────────────────
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# 定时调度（由 websocket_client 启动）
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# ──────────────────────────────────────────
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async def scheduler():
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"""每天 SEND_HOUR:SEND_MINUTE 触发日报"""
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logger.info("[DailySummary] 定时日报已启动，发送时间 %02d:%02d", SEND_HOUR, SEND_MINUTE)
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sent_today: Optional[str] = None # 记录已发日期，防重复
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while True:
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now = datetime.now()
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today = now.strftime("%Y-%m-%d")
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if now.hour == SEND_HOUR and now.minute == SEND_MINUTE and sent_today != today:
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sent_today = today
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try:
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await send_daily_summary(today)
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except Exception as e:
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logger.exception("[DailySummary] 日报生成出错: %s", e)
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# 每 30 秒检查一次
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await asyncio.sleep(30)
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# ──────────────────────────────────────────
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# 命令行手动触发
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# ──────────────────────────────────────────
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if __name__ == "__main__":
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import sys
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target = sys.argv[1] if len(sys.argv) > 1 else ""
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result = asyncio.run(send_daily_summary(target))
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logger.info("\n=== AI 摘要 ===")
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logger.info(result)
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